Free AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Amazon AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Practice Test Question - Page 5

Question 13 (Topic 1)

A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable. Which factor relates to the explainability of the AI solution's decisions?

Select an option, then click Submit answer.

○ Model complexity
○ Training time
○ Number of hyperparameters
○ Deployment time

Question 14 (Topic 1)

A company wants to build a lead prioritization application for its employees to contact potential customers. The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise. Which ML model type meets these requirements?

Select an option, then click Submit answer.

○ Logistic regression model
○ Deep learning model built on principal components
○ K-nearest neighbors (k-NN) model
○ Neural network

Reference / correct answer:

Logistic regression model

Most accepted answer: A. Logistic regression model

Community votes: A=6

Selected Answer: A A: Logistic regression model Explanation: A logistic regression model is interpretable and allows direct adjustment of the weights assigned to different variables (features). This aligns with the requirement for employees to view and modify the weights based on their domain knowledge and expertise. Logistic regression provides a clear relationship between input features and output predictions, making it ideal for use cases that demand transparency and control. upvoted 6 times

Selected Answer: A A is the correct answer. upvoted 1 times

Selected Answer: U A resposta correta é A. Modelo de regressão logística. A regressão logística é ideal porque permite interpretar e ajustar facilmente os pesos atribuídos às variáveis. Os funcionários podem visualizar a influência de cada fator no modelo e ajustá-los manualmente conforme o conhecimento do domínio, tornando a priorização de leads mais transparente e customizável. upvoted 1 times

Selected Answer: A Provides interpretable weights that can be manually adjusted based on domain expertise. upvoted 4 times

Selected Answer: A The correct answer is A. Logistic regression models allow for easy interpretation and adjustment of weights. upvoted 1 times

Question 15 (Topic 1)

A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?

Select an option, then click Submit answer.

○ Supervised learning
○ Unsupervised learning
○ Reinforcement learning
○ Active learning

Reference / correct answer:

Supervised learning

Most accepted answer: A. Supervised learning

Community votes: A=5

Selected Answer: A A: Supervised learning Explanation: Supervised learning is the appropriate method when a dataset of labeled examples is available, as it involves training a model using input-output pairs. In this case, the labeled images of animals (input) and their corresponding categories (output) make supervised learning the ideal approach. The model learns from these examples to classify new, unseen images into the correct categories. Why not the other options? B: Unsupervised learning: Unsupervised learning does not use labeled data and is typically used for clustering or pattern discovery. It is not suitable for this classification task, which requires labeled data. upvoted 5 times

Selected Answer: A Why the others are incorrect: B. Unsupervised learning → used when data is unlabeled. C. Reinforcement learning → used for decision-making through rewards and penalties, not image classification with labeled datasets. D. Active learning → involves iteratively labeling additional data, but the company specifically said it will not label more data. upvoted 1 times

Selected Answer: A A is the correct answer. upvoted 1 times

Selected Answer: A Best for training a model with a labeled dataset, like the company's labeled images. upvoted 1 times

Selected Answer: A A. Supervised learning Explanation: Since the company has a large dataset of labeled images, supervised learning is the appropriate choice. In supervised learning, a model is trained on a labeled dataset, where the input data (images) is paired with corresponding labels (the types of animals). This approach allows the model to learn from the labeled data and make predictions on new, unseen data. upvoted 1 times

Free AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 – Amazon AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Practice Test Question